Fused Machine Learning Modeling of Concrete Creep with Strength Classification
MEI Shengqi1,2,*, LIU Xiaodong3, WANG Xingju2, LI Xufeng3, NIE Liangtao2, KANG Xuejian2
1 State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang 050043, China 2 School of Traffic and Transportation, Shijiazhuang Tiedao University, Shijiazhuang 050043, China 3 School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Abstract: This study establishes a high-accuracy prediction model for concrete creep through strength classification, employing a fused machine learning approach (Stacking) to address the limited adaptability of existing models across varying strength grades. The Stacking framework, composed of base learners and a meta-learner, integrates predictions from multiple base models through meta-learning to enhance predictive performance. First, outlier detection and feature selection were performed on the creep database. The outlier detection process identified and removed 1 080 outliers in creep compliance measurements from the NU-ITI database. After feature selection, 13 parameters were chosen as input variables for the machine learning model. Second, the NU-ITI database was divided into normal-strength concrete (NSC, fc＜60 MPa) and high-strength concrete (HSC, fc ≥60 MPa) datasets to conduct comparative analysis of four base learners’ predictive performance. Results indicated XGBoost’s superior accuracy for NSC and CatBoost’s optimal performance for HSC. Thus, these two algorithms were adopted as the base learners in the Stacking model. Subsequently, both the linear regression (LR) and ridge regression (RR) were tested as meta-learners in the Stacking model. The stacking model with LR and RR as meta-learners achieves comparable accuracy (R2=0.982 6, MAE=3.29 με/MPa, RMSE=5.18 με/MPa), while outperforming all four base learners and existing creep prediction models in the literature. Finally, the SHapley Additive exPlanation (SHAP) method was used to identify key parameters influencing concrete creep in NSC and HSC. Compressive strength, temperature, aggregate-to-cement ratio, and ambient relative humidity are the primary parameters governing the differences in creep mechanisms between two strength grades. Additionally, further validation using the MC2010 and B4 models confirmed that integrating SHAP-based feature importance significantly improves the accuracy of concrete creep predictions.
梅生启, 刘晓东, 王兴举, 李旭峰, 聂良涛, 康学建. 考虑强度分类的融合机器学习混凝土徐变建模[J]. 材料导报, 2026, 40(7): 24100121-8.
MEI Shengqi, LIU Xiaodong, WANG Xingju, LI Xufeng, NIE Liangtao, KANG Xuejian. Fused Machine Learning Modeling of Concrete Creep with Strength Classification. Materials Reports, 2026, 40(7): 24100121-8.
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2026, Vol. 40 
